Method for operating a telecom system

ABSTRACT

Disclosed is a method for managing the operation of a telecom system, and minimizing the energy to be drained from a power supply. According to the method, a rate constraint and telecom environment conditions are determined. Then, a working point is selected a plurality of predetermined working points based on the rate constraint and the telecom environment conditions. The telecom system is operated at the selected working point by setting corresponding control parameters.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is related to a method for operating a telecomsystem, more in particular a wireless system, and devices suitedtherefor.

2. Discussion of Related Technology

By discussion of technologies and references in this section, Applicantsdo not admit that the references are prior art of the inventiondisclosed in this application.

The current demand in increasing data rate and quality of service inadvanced wireless communications has to cope with an energy budgetseverely constrained by autonomy and ubiquity requirements. Trading offperformance and energy consumption deserves the highest attention toenable the ‘anything, anywhere, anytime’ paradigm.

The following observations show the need to integrate theenergy-efficient approaches across layers. First, state-of-the-artwireless systems devices are built to operate at only a fixed set ofoperating points and assume the worst-case conditions at all times.Irrespective of the link utilization, the highest feasible PHY rate isalways used and the power amplifier operates at the maximum transmitpower. Indeed, when using non-scalable transceivers, this highestfeasible rate results in the smallest duty cycle for the poweramplifier. Compared to scalable systems, this results in excessiveenergy consumption for average channel conditions and average linkutilizations. However, recent energy-efficient wireless system designsfocus on energy-efficient VLSI implementations and adaptive physicallayer algorithms to adjust the modulation, code rate or transmissionpower. For these schemes to be practical, they need to be aware of theinstantaneous user requirements.

Further, to realize sizeable energy savings, systems need to shutdownthe components when inactive. This is achieved only by tightly couplingthe MAC to be able to communicate traffic requirements of a single userand schedule shutdown intervals.

In the case of a multi-user wireless communication system, there existcomplex trade-offs between the adaptive physical layer schemes and therequirements of multiple users. For example, lowering the rate of oneuser affects the available time for the second delay sensitive user.This forces the second user to increase its rate, consume more energyand potentially suffer from a higher bit error rate.

However, the traditional approaches, including most of thestate-of-the-art cross-layer optimization frameworks, do not yet enablea meaningful trade-off between performance and energy consumption.Indeed, most of them solve problems in an ad hoc way, focusing on theinteraction between adjacent layers and do not raise the scope to theuser level. Indeed, the real performance metrics are those quantifyingthe quality of the service provided by the complete communication stackto the application, while the only effective energy consciousnessindicator is the actual energy that is drained from the battery. Bothdepend jointly on the propagation aspects, the physical layer, thecomplete protocol stack, the application itself and, moreproblematically, also on their implementation aspects. This spans farmore than the scope of traditional system optimization approaches.Furthermore, the traditional ‘optimization paradigm’ itself, namelyfinding a unique optimal communication system configuration representingthe best trade-off between performance and cost, becomes inappropriatewhen the dynamics in the wireless environment and user requirements areconsidered. More specifically, because of this dynamics, no uniqueoptimal working point exists. The ultimate energy-efficient system wouldhave to adapt permanently its characteristic, given the environmentconstraints, to provide the performance exactly required by the userwith the minimum energy.

To achieve this goal flexible systems must be specified having so-calledconfiguration knobs that can be set at run-time to steer jointly theperformance and energy consumption. The higher the flexibility, i.e. thenumber of configuration knobs across all layers, the higher thepotential gain due to a better match between the system behavior, theenvironment and the real user requirements. However, a penalty existsdue to the required shift, at least partially, of the optimizationprocess to run-time. This is very challenging due to the combinatorialcharacter of the problem (the number of candidate configurations risesexponentially with the number of controlled knobs).

Recently, joint transmission power and rate control has been consideredto reduce system power (see D. Qiao et al., ‘Energy Efficient PCFOperation of IEEE802.11a WLAN with Transmit Power control’, ElsevierComputer Networks (ComNet), vol. 42, no. 1, pp. 39-54, May 2003 and‘MiSer: An Optimal Low-Energy transmission Strategy for IEEE 802.11a/h’,Proc. ACM MobiCom '03, San Diego, September 2003). This approach can beseen as the application to wireless system design of the ‘power aware’design paradigm proposed by Sinha et al. (‘Energy Scalable SystemDesign’, Trans. on VLSI Systems, April 2002, pp. 135-145). Given thefact that transmitting at a lower rate requires less power, the ‘lazyscheduling’ principle has been proposed (see ‘Adaptive Transmission forEnergy Efficiency in Wireless Data Networks’, E. Uysal-Biyikoglu, Ph.D.Thesis, Stanford, June 2003): based on a look-ahead of the linkutilization (i.e. packet arrival at the transmitter), the minimumaverage rate to satisfy the current traffic requirements is consideredand transmit rate and power are set in function of the channel state inorder to achieve this average during the next look-ahead window.

In ‘Energy-aware Wireless Communications’ (C. Schurgers, Ph.D. thesis,University of California, Los Angeles, 2002) the concept of energy-awareradio-management is developed. It proposes simple models to capture theenergy consumption of radio systems that are used to derive someenergy-efficient algorithms to select the modulation order, the coderate and to schedule the packets. This dynamic modulation and codescaling is proposed as a practical way to implement lazy scheduling. Italso discusses the energy trade-off between transmission rates andshutting off the system. Operating regions are derived when atransceiver may sleep or use transmission scaling for time-invariant andtime-varying channels. However, the general solutions to transparentlyexploit the energy-performance scalability at run-time are limited to afew (2-3) system level knobs. The energy-scalability of a system can bedefined as the range in which the energy consumption can vary when theperformance requirements—e.g. the user data rate—or the externalconstraints—e.g. the propagation conditions—vary from worst to bestcase.

In ‘Practical Lazy Scheduling in Sensor Networks’, R. Rao et al, ProcACM Sensor Networks Conf, Los Angeles, November 2003 a CSMA/CA MACprotocol based on the lazy scheduling idea is derived.

From a theoretical point of view, the ‘lazy scheduling’ concept isattractive. E.g. radio link control based on ‘lazy scheduling’ looks tobe a promising technique for WLAN power management. However, it has beenanalyzed exclusively from the viewpoint of physical, MAC and dynamiclink control (DLC) layer. Yet, effective power management in radiocommunication requires considering the complete protocol stack and itscross-layer interactions.

SUMMARY OF THE INVENTION

The present invention provides a method for operating a telecom system,more in particular a wireless system, with a globally optimized powerconsumption for a given quality of service. The invention further aimsto provide devices suited therefore.

The present invention relates to a method for managing the operation ofa telecom system and minimizing the energy to be drained from a powersupply, comprising the steps of: determining a rate constraint;determining the telecom environment conditions; selecting a workingpoint by solving an optimization problem, taking into account the rateconstraint and the telecom environment conditions, and given a pluralityof predetermined working points for a discrete set of telecomenvironment conditions; and operating the telecom system at the selectedworking point by setting corresponding control parameters. Settingcontrol parameters implies imposing said control parameters on thetelecom system.

The telecom system is preferably a wireless telecom system. In anadvantageous embodiment the telecom system is a single telecom device.Alternatively, the telecom system is a plurality of telecom devices. Theoperating step then includes setting each of the telecom devices at aselected working point by setting corresponding control parameters.

In a preferred embodiment the rate constraint is a varying rateconstraint. The rate constraint is preferably a constraint on theaverage rate.

Typically the telecom environment conditions include channel state.Advantageously the discrete set of telecom environment conditions isorganized per channel state.

In another preferred embodiment the step of selecting a working point,includes selecting the plurality of predetermined working pointscorresponding to the determined telecom environment conditions.

In a preferred embodiment, before the step of selecting, the step isperformed of determining the plurality of predetermined working points.Also, before the step of selecting, the step may be performed of loadingthe plurality of predetermined working points. Preferably, after thestep of loading the step is performed of adapting the plurality ofpredetermined working points. The predetermined set of working pointstypically includes at least sleep mode and working mode of the telecomdevice. In a specific embodiment the plurality of predetermined workingpoints define a monotonous, non-convex curve.

The telecom environment conditions preferably comprise path loss and/orchannel frequency selectivity.

In yet another embodiment the control parameters comprise parameterscontrolling modulation order and/or code rate and/or transmit powerand/or packet size.

Advantageously for each of the channel states performance-energytrade-off curves are derived. In an advantageous embodiment theperformance-energy trade-off curves are Pareto-optimal curves.Specifically the energy-per-bit is used as energy metric. The netthroughput may be used as performance metric. Alternatively the sum ofthe energy consumption of the telecom devices is used as energy metric.

In a further embodiment the telecom environment conditions furthercomprise current traffic requirements, whereby the current trafficrequirements are taken into account in determining the rate constraint.

In a further embodiment said step of selecting a working point includessolving a scheduling problem, preferably said scheduling involvesscheduling transmission of packets between said telecom devices, saidscheduling taking into account dependencies between said packets.

The invention also relates to a method for managing the operation of atelecom system comprising a queuing mechanism introducing a queuingdelay, whereby the method minimizes the energy to be drained from apower supply, comprising the steps of: determining telecom environmentconditions, including current traffic requirements, determining anaverage rate constraint to meet the current traffic requirements, bysolving an optimization problem, setting at least one control parameter,the control parameter taking in account the instantaneous queuing delay,operating the telecom system by setting the control parameter taking inaccount the instantaneous queuing delay.

Preferably the average rate constraint is set as a parameterizablefunction of the number of bits in the queue of said queuing mechanism,the parameters being control parameters. Advantageously the step ofdetermining the average rate constraint is based on a look-ahead withvariable window size of the link utilization, whereby the window size isdetermined by the control parameter. In a specific embodiment thetelecom device is further provided with a packet retransmissionmechanism and whereby the optimization problem optimizes the end-to-endthroughput.

Another aspect of the invention relates to a device operating accordingto the method as previously described, comprising storage means forstoring the performance/energy trade-off curves.

In a preferred embodiment the device further comprises computation meansfor solving the optimization problem. Advantageously, the computationmeans further determine or adapt the predetermined working points.

In a further aspect the invention relates to a computer program, storedon a computer readable medium, comprising instructions, which whenexecuted on a computer, executed the methods as previously described.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 represents the energy consumption breakdown in typical wirelesstransceivers.

FIG. 2A represents DC power consumption of a Class A power amplifier(PA).

FIG. 2B represents adaptation of the PA gain compression characteristic.

FIG. 3 represents the physical layer simulation chain used to derive thePER performance model.

FIG. 4A represents block error rate performance of 8 states of thefading channel realizations.

FIG. 4B is a histogram of the state probabilities of the 8 states of thefading channel realizations.

FIGS. 5A-5G are the optimal energy performance trade-off curves for a802.11a WLAN link at 10 m, for standard transceivers (dashed line) andenergy-scalable transceiver (plain line).

FIG. 6 represents a variable rate transmission system model.

FIG. 7 represents the average power versus average delay trade-off forthe optimal (hypothetical) policy (square), the original lazy scheduling(circle) and the proposed proportional control policy (triangle).

FIG. 8 represents the average power vs. average delay for different linkload and different values of K. The delay axis is normalized by thecontrol period.

FIGS. 9A-9K represent the average delay as a function of the controlparameter K in various input bit rates. Up to 80% utilization, theaverage delay is inversely proportional to K. At higher utilization, theproportionality is lost, the queuing behavior becoming non-linear due tocongestion of the channel.

FIG. 10 represents a wireless point-to-point file transfer scenario.

FIG. 11 represents the packet drop probability as a function of K.

FIG. 12 represents TCP throughput per connection (i.e. one out of the 10connections sharing the link) as a function of the control parameter K.

FIG. 13 represents the energy per byte versus TCP throughput trade-offachievable by tuning the link scheduling policy.

FIG. 14 represents a distributed stack-wide power management.

FIG. 15 represents Pareto curves obtained after calibration for a pathloss of 80 dB.

FIG. 16 represents the average power versus average goodput trade-offachieved by the proposed radio link control strategy (circle) comparedto the traditional goodput maximizing strategy. The plain linecorresponds to the optimal selection.

FIG. 17 represents a centrally controlled LAN/PAN topology illustratinguplink and peer-to-peer communication.

FIG. 18 represents the determination of a pruned C_(i)(R_(i)) mapping tobe used at runtime, starting from a cost and resource profile.

FIG. 19A represents the performance across different channel states.

FIG. 19B is a channel state histogram of a probability of occurrencebased on the channel realizations database.

FIG. 20 represents the timing of successful and failed uplink frametransmission under the HCF and the scheme according to the invention.

FIGS. 21A and 21B represent the mapping for the PA output power andback-off control dimension for a fixed setting of the modulation andcode rate control dimensions.

FIG. 22A represents C_(i)(R_(i)) curves in different channel states for1 fragment.

FIG. 22B represents C_(i)(R_(i)) curves for different frame sizes inchannel state 1.

FIG. 23 represents the two-frame buffering.

FIG. 24A represents expected energy consumption across different channelstates for 1 fragment frame size.

FIG. 24B represents relative energy consumption by sleeping and scalingfor different system loads in the best channel state.

FIG. 25A represents energy consumption per flow as a function of theaggregate system load for CBR traffic.

FIG. 25B represents energy consumption per flow as a function of meanper-flow data rate for MPEG traffic.

FIG. 26A represents energy consumption for CBR traffic over atime-variant channel as function of aggregate system load

FIG. 26B represents energy gains for sleeping versus scaling fortime-variant channel.

FIG. 27 is a flowchart of an embodiment for managing the operation of atelecom system.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The following detailed description will explain various features of theinvention in detail. The invention can be embodied in numerous ways asdefined and covered by the claims. In order to clarify certain parts ofthis description some concepts will be illustrated with the practicalexample of an OFDM-based WLAN system.

To illustrate certain elements of the solution according to theinvention, the energy-scalability of OFDM-based WLAN such as proposed bythe 802.11a/g standards will be considered as an example. Currentlyproposed techniques to exploit the energy-performance trade-off inwireless links mainly rely on the reduction of the transmit-power.However, when considering the energy consumption breakdown of practicalIEEE 802.11a WLAN transceivers (FIG. 1), it appears that thetransmit-power itself accounts only for 8% of the total energyconsumption of the transmitter. The Power Amplifier (PA) dominates with49%. Moreover, due to the stringent linearity and spectral maskrequirements of the OFDM modulation, class A power amplifiers are mostoften required. Consequently, the actual energy consumption of the PA isinvariant with the transmit power. On the other hand, traditionalreceiver architectures are dimensioned for the most demanding modulationand present constant processing gain. Therefore, only the duty cycleeffectively affects the link energy consumption. In that context, thehighest modulation order that goes through the channel with reasonablepacket error rate (i.e. 1%) is always the best choice. Performance (i.e.net data rate) downscaling does not bring effective energy consumptionreduction.

In order to get effective energy consumption benefit from energy-awarelink adaptation technique, the energy consumption scalability ofOFDM-based WLAN transceivers has to be enhanced. The followingenhancements are proposed: A. configurable power amplifier saturationpoint; and B. configurable receiver processing gain.

A. Configurable Power Amplifier Saturation Point

The transmitter energy-consumption is dominated by the power amplifiercontribution. The idea is to allow adapting the gain compressioncharacteristic of the power amplifier together with its working point onthis characteristic. Typical WLAN power amplifiers operate with class Aamplifiers that have a fixed gain compression characteristic, so that itis impossible to reduce simultaneously the output power and thelinearity, e.g. to adapt to the lower requirement of lower ordersub-carrier modulations, as illustrated in FIG. 2B. Reducing the outputpower translates into an increased back-off, and therefore into anhigher linearity and a decreased power efficiency. This kills thebenefit in terms of total power consumption. Therefore, it makes senseto vary independently the average transmit power and the back-off, whichrequires to adapt the gain compression characteristic (FIG. 2A). Thiscan be done for instance by applying dynamic voltage scaling.

B. Configurable Receiver Processing Gain

For the receiver, it appears that the energy consumption is dominated bythe digital signal processing in which the forward error correctionaccounts for an important part (FIG. 1). Scalability in receiving energyconsumption can be achieved by trading off processing gain (andconsequently, receiver sensitivity) and computing energy by activatingmore or less digital signal processing. This can be achieved forinstance by adopting iterative forward error correction (FEC) schemeslike turbo-codes. Turbo-codes can be implemented with low energyconsumption and provide a wide freedom to trade-off energy consumptionversus code gain, which translates in receiving processing gain.

The impact of the energy-scalability enhancement techniques is nowfurther analyzed. The optimal trade-off between the net data rate on topof the data-link layer (goodput) and the total link energy consumptionis derived by exploring the settings of the traditional link parameters:modulation, code rate, output power, packet size next to the newfunctional parameters introduced, namely the power amplifier saturationpower relative to the output power (back-off) and the number of decodingiterations. Table 1 summarizes the system level control knobs used tobring energy-scalability and their range.

TABLE 1 PA transmit Power (P_(Tx)) 0 to 20 dBm by step of 2 dBm PA backoff (b) 6 to 16 dB by step of 1 dB Packet (data) size (L_(p)) 50, 100,500, 1000, 1500 bytes Code rate (R_(c)) ½, ⅔, ¾ Modulation order(N_(mod)) 1, 2, 4, 6 Number of turbo-iterations (I) 3 to 6

To be able to explore the energy-scalability of extended WLANtransceivers, robust performance and energy models are developed, whichalso consider the specificity of the indoor propagation channel. Themodels are incorporated in a simulation framework that allows generatingvery quickly performance-energy trade-off curves in realistic userscenarios, e.g. Pareto curves. These models are now discussed more indetail.

Tracking analytically the dependencies between the link parameters(system level knobs), the environment constraints (here the path lossand the channel frequency selectivity), the energy consumption and theperformance (data rate) is difficult when the complete system isconsidered (i.e. not only the physical layer). Performance and energyconsumption are non-linearly coupled e.g. by the automatic repeatrequest (ARQ) mechanism. To capture accurately those effects, systemprofiling using event-driven protocol simulation is most appropriate.Yet, the protocol simulator requires information about the radio-link,i.e. the packet error rate (PER), the gross data rate and thetransmission energy per packet, which is also dependent on the systemlevel knobs and the environment constraints.

At the physical layer level performance and energy models can bedecoupled. An end-to-end simulation chain (FIG. 3) implementing theenergy-scalability enhancement techniques as described before is used toidentify the physical layer PER performance as a function of the systemlevel knobs. First the data is encoded by a turbo-encoder. Then, the bitstream is modulated using OFDM according to the IEEE 802.11a standard.When transmitted, the signal is hampered by the non-linearity of thepower amplifier. This effect is captured considering an additivedistortion power, the distortion being assimilated to a noise source(D). The transmitter output signal-to-noise and distortion ratio (SINAD)is related to the back-off according to a law obtained by measurements.Next, the signal passes through the channel. The effect of the wirelessindoor channel is decoupled in two effects: first, an average path lossassumed constant (a(d)); secondly, the multi-path fading that leads to atime-varying frequency selective channel response. To model the noiseintroduced by the receiver, a white Gaussian noise (n) is added.Finally, the signal is demodulated and decoded. The Packet error rate(PER) can be evaluated with Monte-Carlo simulations. However, it isbetter to assess the code block error rate (BIER), which is independentof the packet size. The PER can be directly derived from the BIER whenknowing the packet size.

Simulation results depict a large variation in performance depending onthe channel realisation, even when the average path loss is constant. Byperforming Monte-Carlo simulations over a large amount of channelrealizations, a classification has been made (FIGS. 4A and 4B). Channelresponses leading to similar BIER for the same SINAD have been groupedin representative classes corresponding to so-called channel states. Itturns out that 8 classes are sufficient to cover the range withrepresentative average BIER vs. SINAD curves. FIG. 4A depicts the blockerror rate performance curves for the first 7 states considered. Thehistogram of the state probabilities is depicted in FIG. 4B. Theseparation between two adjacent channel classes is set to 2 dB, whichcorresponds to the granularity with which it is assumed the transmitpower can be tuned. No transmission is assumed to be possible in the8^(th) channel state, which occurs with a probability of less that 0.5%.With such a model, the energy-performance trade-off can be analyzedindependently for each channel state, which again reduces theexploration complexity.

Next to the performance model, an energy model for the wireless link hasto be established. The energy to transmit and to receive one packet isgiven as a function of the system level knobs (Table 2).

For the transmitter, the energy per packet is assumed equal to theproduct of the radio power by the time needed to transmit the packet(T_(up)), plus the energy consumed by the digital processing to formatit, which is proportional to the packet size. EDSP denotes the DSPenergy consumed per transmitted bit. The radio power is the sum of thePA power (P_(PA)) and radio front-end power (P^(T) _(FE)). The PA power(P_(PA)) is equal to the transmit power (P_(Tx)) divided by the PA powerefficiency (η), which is expressed as a function of the back-off b bythe measured relation η(b). This relation has been obtained by fittingto measurement results. The contribution of the transmit front-end(P^(T) _(FE)) is assumed to be constant. The time to transmit a packet(T_(up)) can be computed as the ratio of the packet size (L_(p)) by thephysical layer data rate, which is the product of the modulation order(N_(mod)), the number of data carriers in the OFDM symbol (N_(c)), thecode rate (R_(c)) and the OFDM symbol rate, or baud rate (R_(s)).

TABLE 2 Performance model Energy model Model Parameters L_(b) Turbo CodeBlock size (bit) 288 P^(T) _(FE) Transmit front -end power (W) 200 WBandwidth (MHz) 20 P^(R) _(FE) Receive front -end power (W) 200 TTemperature (Celsius) 25 E^(R) _(DSP) Receive DSP energy (nJ/bit) 7.5 NFReceiver Noise Figure (dB) 15 E^(T) _(DSP) Transmit DSP energy (nJ/bit)8.8 (Including Implemtation loss) R_(s) Baud Rate (kbaud) 250 d DistanceTransmitter -Receiver (m) 10 N_(c) Number of data carrier per OFDMsymbols 48 Relations A(d) Channel attenuation Front average prop agationR_(bit) OFDM bit rate (bps) R_(bit) = N_(c) · N_(mod) · R_(c) · R_(s)loss model D(b) PA distortion Fitted on measurement on microsemi PAT_(up) Transmit/Receive on time per packet (s)$T_{up} = \frac{L_{p}}{R_{int}}$ N Receiver noise kT · W · NF η(b) PApower efficiency Fitted on measurement microsemi PA SINAD Signal tonoise and distortion ratio${SINAD}_{e} = \frac{P_{tc} \cdot A}{{{D(b)} \cdot A} + {{kT} \cdot W \cdot {NF}}}$P_(PA) PA power (W) $P_{PA} = \frac{P_{pTz}}{\eta(b)}$ BlER Code BlockError Rate BIER = f(SINAD · Nmod · Rc · 1) Fitted on Monte -CarloSimulation Results E_(FEC) Energy turbo -decoding (i/bit)$\begin{matrix}{{E_{FEC} = {3.98 \cdot 10^{- 9}}}{{\cdot L_{B}} + {1.21 \cdot 10^{- 7} \cdot I} +}} \\{{1.32 \cdot 10^{- 9} \cdot L_{b} \cdot I} - {3.69 \cdot 10^{- 7}}}\end{matrix}\quad$ PER Packet Error Rate${PER} = {1 - \left( {1 - {BIER}} \right)^{\frac{L_{p}}{L_{b}}}}$ E_(TX)Total Transmit energy (J/bit)E_(Tx) = (P_(PA) + P_(FE)^(T)) ⋅ T_(up) + E_(DSP)^(T) ⋅ L_(p) E_(Rx)Total Receive Energy (J/bit)E_(Rx) = P_(FE)^(R) ⋅ T_(up) + (E_(DSP)^(R) + E_(FEC)) ⋅ L_(p)

For the receiver, on the other hand, the energy per packet is modelledas equal to the analogue receiver power (P^(R) _(FE)) multiplied by theT_(up), plus the digital receiver energy per packet, including theturbo-decoding energy expressed in function of the number of iterationsand the code block size (N). The time to receive the packet is computedthe same way as for the transmitter.

Table 2 summarizes the main relations and parameters of the performanceand energy models. Parameter values have been captured from measurementscarried out on a real advanced WLAN set-up.

As performance metrics the user data rate on top of the data linkcontrol layer (DLC) is considered and as energy consumption metric, thetotal energy to transmit successfully a packet. To be able to profilethose metrics as a function of the system level knobs settings,considering the impact of the MAC and DLC protocols, the physical layermodels are plugged into a network simulator. A two-user scenario hasbeen defined. To evaluate the average user data rate (defined on top ofthe data link layer) and the corresponding energy consumption, the inputqueue of the transmitter is filled with 10000 packets and profile thetotal time and energy consumption needed to receive them correctly inthe receiver output FIFO.

The simulation is carried out for every combination of the system levelknobs described earlier. This leads to a variety of energy-performancetrade-off points from which only the Pareto-optimal ones are retained.The latter points form a so-called optimal trade-off curve (also calledPareto curve) that gives the minimum energy to achieve a givenperformance in the considered channel state. Such trade-off curves aregenerated for standard and energy scalable systems on the 7representative channel states. Results are depicted in FIGS. 5A-5G. Theenergy per transmitted bit is considered so that the results areindependent of the link duty cycle. For the standard system, the 1-dBgain compression point is assumed to correspond to an output power of 36dBm, thus a back-off of 16 dB for the maximum output power (20 dBm).Standard convolutional codes are considered according to the 802.11astandard. Modulation order, code rate and transmit power are assumed tobe adaptive as in state-of-the-art link adaptation schemes. Packet sizeis also assumed to be adaptive, which reveals to have impact only on theworst-case channel states. For the energy scalable system, theadditional system level knobs are varied according to the ranges givenin Table 1. Both experiments have been carried out assuming an averagepath loss of 81 dB, corresponding typically to a distance of 10 m. FromFIG. 6, it can be seen that with the standard system, it is alwaysbetter to use the highest modulation order and code rate that go throughthe channel with reasonable packet error rate (i.e. 1%). This is due tothe fact that the increase in energy per bit relative to the usage of ahigher order modulation is compensated by the reduction in duty cycleconsecutive to the increase in spectral efficiency. In that case, powermanagement techniques based on duty cycle reduction are more effective.

The implication effect of ‘lazy scheduling’ on the end-to-endperformance of a wireless network will now be analyzed. Further, it isinvestigated how the end-to-end performance versus energy can beeffectively controlled by adapting the link layer ‘lazy scheduling’policy. The performance on top of the transport layer is considered.Indeed, it is the latter that actually provides the communicationservice to the application. Application specific performance metrics arenot considered, but the TCP transport protocol is used, which is by farthe most used in the Internet. The impact of variable error rate hasalready been analyzed (see L. Zou et al., ‘The effects of AdaptiveModulation on the TCP Performance’, Communications, Circuits and Systemsand West Sino Expositions, pp. 262-266, 2002) but the consequence of thevariable rate that would be introduced by ‘lazy scheduling’ has not. TCPthroughput degradation resulting from varying rate and delay isdiscussed in Choi et al. (‘TCP Performance Analysis in WirelessTransmission using AMC’, IEEE VTC Spring, 2003) for CDMA mobile systemsusing adaptive modulation and coding. However, the possible control ofthis degradation and the trade-off with energy are not discussed in anyof these related works.

From Shannon one knows that the minimum power P required to reliablytransmit on a given (Gaussian) channel (characterised by a given signalto noise ratio and bandwidth) is a monotonously increasing, convexfunction of the targeted data rate R. This function is given by equation(1) where R_(s) is the symbol rate (baud rate), A and α the constant(average path loss) and variable (fading) components of the channelattenuation. N_(o) is the noise power density.

$\begin{matrix}{P = {\frac{N_{0}R_{s}}{A\;\alpha} \cdot \left( {2^{\frac{R}{R_{s}}} - 1} \right)}} & \left( {{equation}\mspace{14mu} 1} \right)\end{matrix}$

When the channel presents a time-varying attenuation, the signal tonoise ratio varies accordingly and consequently the feasible rate for agiven transmit power. A radio link control scheme is designed thatallows finely controlling the link performance versus transceiver powerconsumption trade-off by adapting automatically, per frame, the discretelink control knobs introduced previously (modulation, code rate,transmit power and linearity) to tractable channel state information.Adapting the transmit rate and power to time-varying channel conditionsin order to maximize the average data rate under an average powerconstraint is a well-understood problem referred to as ‘linkadaptation’. Optimal rate and power allocation schemes have beenproposed. The dual problem, i.e. minimizing the average power under anaverage rate constraint can effectively be considered for powermanagement. This can be seen as an application of the ‘power aware’design paradigm where performance is traded off with energy. From thedata link layer point of view (where normally rate and power control areimplemented), the performance is traditionally evaluated in terms of netthroughput (goodput), which is the net average data rate consideringpossible retransmissions. When a variable rate is considered, link delaybecomes a second important performance metric. Indeed, as shown in FIG.6, a queue has to be introduced to deal with the transmission ratevariation. This queue introduces a delay that dominates the transmissionlatency at the bottleneck link. It can be shown (Little law) that theaverage queuing delay ( D) equals the average queue backlog ( Δ) dividedby the average rate on the link ( R).

The energy versus queuing delay trade-off in such systems is extensivelystudied in ‘Power and Delay Trade-offs in Fading Channels’, Berry, Ph.D.Thesis, MIT, Cambridge, Mass., December 2000. Long-term average powerand queuing latency are considered. Using dynamic programming, policiesto specify for each packet, depending on the actual queue backlog andchannel gain, which rate to use are characterised. It is shown thatqueue stability can be guaranteed—i.e. the maximum number of bits in thequeue is bounded—and the average energy versus average delay trade-offis bounded.

‘Lazy scheduling’ is an example of such a policy. The principle of lazyscheduling consists of looking ahead at the packet arrivals, consideringa fixed time window (T_(w)). At the end of each time window, the actualqueue backlog (?_(w)) is considered to compute an average rateconstraint (eq. 2), which is used to compute the transmit rate and poweras a function of the channel attenuation for the next window. The‘water-filling in time’ algorithm can be used for that purpose.Initially, this procedure has been derived considering the informationtheoretical relation between transmission rate and power (eq. 1).However, this relation corresponds to a bound that practical physicallayer modulation and coding schemes tend to approach but do not meet.Also, in practice, the rate cannot be varied continuously but onlystepwise, e.g. by varying the modulation order (constellation size) orthe coding rate. Without hampering the generality, the practical rateadaptation algorithm proposed by Schurgers (cfr. supra) is considered.In an embodiment, the adaptation policy must indicate which modulationto use as a function of the channel attenuation. It is shown that for anarrow band Rayleigh fading channel, using Quadrature AmplitudeModulation (QAM) with constellation order 2^(b)=2^(2j), j∈IN, theoptimal policy is given by equation (eq.3), where δ is obtained bysolving (eq.4), R_(s) being the symbol rate of the modulation and Rcorresponding to the average rate constraint. The corresponding power isgiven by (eq.5) where C is a constant depending on the average pathloss, coding gain, receiver noise figure and targeted bit error rate.

$\begin{matrix}\begin{matrix}{\overset{\_}{R} = \frac{\Delta_{w}}{T_{w}}} \\{b_{i} = {2 \cdot \left\lceil {{{\frac{1}{2} \cdot {\log_{2}\left( \frac{\alpha_{i}}{\delta} \right)}} + {1\mspace{14mu}{if}\mspace{14mu}\alpha_{i}}} \geq \delta} \right.}}\end{matrix} & \left( {{equation}\mspace{14mu} 2} \right) \\{b_{i} = {{0\mspace{40mu}{if}\mspace{14mu}\alpha_{i}} < \delta}} & \left( {{equation}{\mspace{11mu}\;}3} \right) \\{{2 \cdot {\sum\limits_{j = 0}^{\infty}\;{\exp\left( {{- \delta} \cdot 4^{j}} \right)}}} = \frac{\overset{\_}{R}}{R_{s}}} & \left( {{equation}\mspace{20mu} 4} \right) \\{P_{i} = {\frac{C}{\alpha_{i}}\left( {2^{b_{i}} - 1} \right)}} & \left( {{equation}{\mspace{11mu}\;}5} \right)\end{matrix}$

Notice further that block fading is assumed, i.e. a constant channelattenuation during the transmission of one packet. This requires thechannel coherence time to be high relative to the transmit duration ofone packet. Hence, the modulation does not have to be adapted during apacket transmission.

TABLE 3 R_(s) 10 MBaud C 60 mW B {1, 2, 4, 6} P_(max) 20 dBm T_(c) 20 ms

Considering this simple but representative model, the energy-delaytrade-off achieved by lazy scheduling is evaluated. The channel state isvaried according to a 8-state finite state Markov model whose transitionmatrix is set so that the probability density function (pdf)approximates a Rayleigh distribution of average 1 and the coherence timeequals T_(c). Further parameters are summarized in Table 3. The maximumaverage rate achievable with this setup, considering the transmit powerlimit of 20 dBm is close to 20 Mbps. In this experience, a constantinput bit rate of 10 Mbps is considered, corresponding to 50%utilization of the link capacity.

Results are depicted in FIG. 7 (circles). They are compared to the bound(optimal policy) computed for the same set-up (square). Clearly, theinitial lazy scheduling performs sub-optimally when the toleratedlatency is low (a short coherence time). To circumvent this drawback,instead of considering a finite time window, an adaptation of theaverage rate constraint with a finer grain is proposed. A proportionalcontrol is considered. The rate constraint is set proportional to thenumber of bits in the queue (eq.6) at a frequency corresponding to thecoherence time (T_(c)).R _(i) =K×Δ _(i)  (equation 6)

Here, R _(i) and ?_(i) denote average rate constraint and average delay,respectively, i denoting a time index. The rate/power adaptation policyis tuned continuously according to this varying average rate constraint( R) in (eq.4). For a low proportionality factor (K), the system willtolerate a large average delay and react softly to the channelvariation. Hence, the adaptation policy will behave close to theunconstrained water-filling in time. However, for high K, the reactionwill be more aggressive, resulting in a lower average delay. For veryhigh K, the system will always tend to select the highest modulationorder possible and adapt the power according to channel inversion (i.e.boost the power for bad channels). Hence, by varying K, one can rangebetween the two extreme link adaptation policies: water-filling in timeand channel inversion.

It can be seen from FIG. 7 (triangle) that the new policy beats theinitial ‘lazy scheduling’ for smaller latencies and stays close to thehypothetic optimal policy in that region. Power-delay trade-offs fordifferent input bit rates are depicted in FIG. 8. It can be seen thatour adaptive policy offers a significant trade-off range for the averagerate up to 80% link utilization. For half load (50% utilization), theaverage power ranges a factor 3 between the extreme adaptation policies.In FIGS. 9A-9I, the average delay for each of the different input bitrate is plotted as a function of the parameter K. It clearly appearsthat this knob enables to control the average delay.

Interestingly, one can notice that the proposed policy is separable: theadaptations to the queue backlog—i.e. to the traffic requirements—and tothe channel can be de-coupled as far as a “constraint propagation” isadded. More specifically, an average rate constraint is derived from thequeue backlog. This constraint is propagated to tune the rate/powerversus channel state adaptation policy. Hence, the solution can beimplemented without necessarily jointly designing the rate/poweradaptation (in practice, the radio link control layer) and the data linkcontrol scheduler (that controls the queue). Those layers can bedesigned separately, the average rate constraint being the minimalcross-layer information to be passed from one layer to another toguarantee close-to-optimal operations.

So far, the energy versus performance trade-off resulting from thedifferent ‘lazy scheduling’ approaches has been studied from the datalink layer viewpoint only. Queuing delay and throughput on a singlewireless link are considered as performance metrics. The differentalgorithms try to minimize the power needed to achieve these givenperformance metrics. Yet, to effectively study the impact of thistechnique on the end-to-end system performance, it is mandatory to alsoconsider the interactions with the higher layers. Indeed, it should beunderstood how a bottleneck link (supposed to be the wireless link)delay and throughput translates into end-to-end system performance. Theend-to-end system considered here consists of a single wireless link,corresponding e.g. to point to point scenario (FIG. 10). This is indeedthe simplest scenario sufficient to show the cross-layer interactions.The single wireless terminal considered runs a number of applications,e.g. file transfers, which most probably use a TCP/IP protocol stack.The impact of an adaptive link layer on the average end-to-end TCPperformance will now be shown. First the mechanism of the protocol isbriefly reviewed. Secondly, it is shown how average end-to-endperformance can be controlled by adapting the ‘lazy scheduling’ policyat link level.

TCP offers a reliable connection to the application. To enable this,acknowledgements are used to inform the source if a packet (identifiedwith a sequence number) is well received. Using this feedback mechanism,it is possible to derive implicitly information about the possiblenetwork congestion, which occurs when the traffic sent through thenetwork is larger than the capacity of the bottleneck link. Networkcongestion translates into excessive queuing delays or eventuallypackets drops at the bottleneck queue. Delays are discovered when theacknowledgement as a response to a sent packet is delayed more thanexpected by the source (i.e. a time-out event). Packet drops aretranslated in the reception of ‘triple-duplicate’ acknowledgements, i.e.the acknowledgements of packets following the lost packets containidentical sequence number. TCP reacts on this by maintaining acongestion window of W packets. Each Round Trip Time (RTT), i.e. thetime between sending a packet and receiving its acknowledgement, TCPsends W packets. During congestion avoidance, the window is increased by1/W each time an ACK is received. A TCP connection can also be in theslow start phase, where the window size is increased more aggressively.As one is mainly interested in the steady-state average behavior of TCP,this phase is not considered for the analysis. Conversely, thecongestion window is decreased whenever a packets loss is detected, withthe amount of the decrease depending on whether packet loss is detectedby a duplicate ACK or by a time-out event. For a duplicate ACK, thewindow size is halved, while for a time-out it is reset to 1.

The steady-state performance of a bulk TCP flow (i.e. a flow with alarge amount of data to send, such as file transfers) may becharacterised by the send rate, which is the amount of data pushed bythe sender per time unit. If the number of packet losses orretransmission is small, the throughput, i.e. the amount of datareceived per time unit, is well approximated by this send rate. Define pto be the packet loss probability of a sent TCP packet. The send rate ofa bulk TCP transfer is well approximated by:

$\begin{matrix}{S = {\frac{1}{RTT}\sqrt{\frac{3}{2p}}}} & \left( {{equation}\mspace{14mu} 7} \right)\end{matrix}$

Considering a wireless link with time-varying rate, if the linkadaptation is done well and MAC retransmissions are allowed, the lossesat the wireless link can be neglected (to e.g. 0.1%). Hence, p isdominated by the losses or delays at the queue. Also RTT is mainlydetermined by the queuing delays at the bottleneck link (i.e. thewireless link). Therefore, both p and RTT depend largely on the linkcontrol parameter K. In FIG. 11, p denotes a range of K. A scenario with10 TCP bulk transfer connections and a queue size of 128 packets isconsidered. The packet size is 1500 bytes. Further simulation parametersare the same as in Table 3. The loss probability p is approximated bydetermining a lower bound of the total number of packets lost ordelayed. Hence, this gives a positive approximation of p. For small K,the throughput is determined by the link delay, which is mainlytranslated into time-out events. A larger K results in more aggressiveTCP flows, filling the queue until a drop event, which is translated inmore duplicate ACK events. The simulated TCP send rate per connection isplotted in FIG. 13, next to the value computed from equation (eq.7). Theshape of the send rate is indeed well approximated by (eq.7), althoughit gives too optimistic values because of the optimistic approximationof p. Based on the link control parameter K, a useful range ofthroughput values is achieved, resulting in an end-to-end energy perbyte versus throughput trade-off as depicted in FIG. 12. Forreadability, both throughput and energy axis are normalized to theirmaximum values (165 KB/s and 2 μJ/byte per connection). Energy scales infactor 2.5 when throughput scales in a factor 2, corresponding to Kranging from 2 to 10.

The above analysis shows that the end-to-end performance for bulk TCPtransfer, i.e. the steady-state end-to-end throughput, is mainlydetermined by the queuing delay, translating into a loss probability pand the round trip time RTT. A performance constraint on top of TCP(throughput) can be translated (by eq.7) into an average delayconstraint on the wireless link, provided that the latter is thebottleneck. When lazy scheduling is considered, this delay can becontrolled either by the look-ahead window size (original lazyscheduling proposal) or by the proportional control parameter (K) in thescheme according to the invention. Also, recall that the proposed linkadaptation policy is separable. From the average delay constraints andthe actual queue backlog, an average rate constraint can be derived(eq.2 or 6) and propagated to the radio link control, which can use itto decide, for each packet, which rate and transmit power to use inorder to minimize power. This observation has important consequences onthe power management design paradigm. Indeed, this shows that efficientpower management trading off energy with effective user-relatedperformance metrics (here the TCP throughput) can be achieved keeping aclean, layered system architecture and its obvious advantage in terms ofmaintainability. Further, unintended interactions between protocolsintroduced by flat cross-layer design are avoided. Stack-wide powermanagement is achieved by coordinating local algorithms by constraintpropagation. The average DLC. queuing delay and average transmissionrate have been shown to correspond to the minimum information passingrequired between the transport layer and data link control, and betweendata link control and radio resource control, respectively. This allowsto draft the structure of a stack-wide power management schemedistributed between the traditional layers (FIG. 14), still performingas well as a flat cross-layer power management scheme.

Referring to FIG. 14, the layers of the distributed stack-wide powermanagement scheme, for example, include the following: applicationlayer, middleware layer, transport layer (TCP), network layer (IP), datalink layer (DL) and physical layer (PHY). The data link layer comprisesradio link control and logical link control. With the distributedstack-wide power management scheme, one can conduct a method of managingthe operation of a telecom system which comprises a queuing mechanismintroducing a queuing delay. The method comprises: determining telecomenvironment conditions, which comprises current traffic requirements;determining an average rate constraint to meet the current trafficrequirements, by solving an optimization problem and setting at leastone control parameter, the control parameter taking into account theinstantaneous queuing delay; and operating the telecom system by settingthe control parameter taking into account the instantaneous queuingdelay. In the method, the average rate constraint is set as aparameterizable function of the number of bits in the queue of thequeuing mechanism. The parameters are control parameters. Thedetermination of the average rate constraint is based on a look-aheadwith a variable window size of link utilization. The window size isdetermined by said control parameter.

At run-time, the energy and performance model are calibrated (initiallyand whenever appropriate, still at a very low rate) to derive the actualenergy/performance trade-off characteristics that are used to carry outthe run-time settings adaptation at the desired (fast) rate. Parametricenergy and performance models being available for the considered system,methods are now derived to carry out the run-time phase. The transmitterconfiguration is assumed to be done just before the transmission of thepacket according to the channel conditions at that moment. The run timephase is split into two steps: a calibration (carried out at lowfrequency) and an effective run-time adaptation step (per packet). Thelatter relies on the calibrated energy/performance characteristics. Bothsteps are analyzed hereafter.

Knowing all parameters, included those measured at run-time (e.g. theaverage path loss), the energy per bit vs. goodput trade-offcharacteristic can be derived for each channel state. From the initialconfiguration space—i.e. the set of combination of control knobssettings (modulation order, code rate, transmit power and powerback-off)—those corresponding to Pareto optimal trade-offs in the energyper bit versus goodput plane are stored in a table. This corresponds tothe so-called calibration. At this point, the energy per bit isconsidered as energy metrics in order to maintain two independent axes.Indeed, for a given configuration, the energy per bit is constant whilethe average power depends on the rate and on the duty cycle. If theknobs setting range is limited, an exhaustive search in theconfiguration space is still possible. If the number of knobs and/or therange is larger, then heuristics should be used. Notice that this searchhas to be performed only when the model is recalibrated (when enteringthe network or when the average path loss changes significantly), so thetime and energy overhead involved in this is not really that critical.FIG. 15 depicts the results of the calibration with the considered modelfor an average path loss of 80 dB, corresponding typically to a distanceof 10 m indoor. One step-curve (Pareto curve) represents thePareto-optimal energy-rate trade-off for a given channel state. Each ofthese points corresponds to a given (N_(mod), R_(c), P_(Tx), b) setting.This implies that selecting one point on one of these curves correspondsto deciding jointly about the carrier modulation (N_(mod)), the coderate (R_(c)), the transmit power (P_(tx)) and the backoff setting (b) tobe used.

The knobs settings corresponding to Pareto-optimal energy-ratetrade-offs are known for each channel state from the calibration. Yet,this is not sufficient to carry out effective energy aware radio linkcontrol. A policy to decide which configuration point to use when agiven channel state is detected, is needed. A trivial policy wouldselect, on the Pareto curve corresponding to the current channel state,the point providing the goodput just larger than the user data raterequirement. Obviously, such a policy is sub-optimal from the powerviewpoint. Indeed, since the constraint is the average goodput, it wouldbe more effective to reduce the rate on the bad channel states (wherethe cost in energy to maintain a given rate is high) and compensate byincreasing it on the good channel state (where the cost is lower). Thisis the principle underlying the water-filling algorithm proposed in ‘TheCapacity of Downlink Fading Channels with Variable Rate and Power’, A. JGoldsmith, IEEE Trans. Veh. Techn., Vol. 46, No. 3, August 1997. Yet,one cannot directly apply this algorithm here due to the discrete natureof the set-up. Therefore, first the structure of the problem isanalyzed.

Let (r_(ij), e_(ij)) be the coordinates of the i^(th) Pareto point onthe curve corresponding to channel state j. The average power P and rateR corresponding to a given radio link control policy—i.e. the selectionof one point on each Pareto curve—can be expressed by (eq.8) and (eq.9)where x_(ij) is 1 if the corresponding point is selected, 0 otherwise.ω_(j) is the probability of the channel state j.

$\begin{matrix}{\overset{\_}{P} = {{\sum\limits_{j}^{\;}\;{\psi_{j}{\sum\limits_{t}^{\;}\;{x_{ij}e_{ij}r_{ij}}}}} = {{\overset{\;}{\sum\limits_{i}}\;{\sum\limits_{j}^{\;}\;{x_{ij}\psi_{j}e_{ij}r_{ij}}}}\overset{̑}{=}{\sum\limits_{i}^{\;}\;{\sum\limits_{j}^{\;}\;{x_{ij}p_{ij}^{\prime}}}}}}} & \left( {{equation}\mspace{14mu} 8} \right) \\{\overset{\_}{R} = {{\sum\limits_{j}^{\;}\;{\psi_{j}{\sum\limits_{i}\;{x_{ij}r_{ij}}}}} = {{\sum\limits_{i}^{\;}\;{\sum\limits_{j}^{\;}\;{x_{ij}\psi_{j}r_{ij}}}}\overset{̑}{=}{\sum\limits_{i}^{\;}\;{\sum\limits_{j}^{\;}\;{x_{ij}r_{ij}^{\prime}}}}}}} & \left( {{equation}\mspace{14mu} 9} \right)\end{matrix}$

The notation p′_(ij) and r′_(ij) is introduced corresponding to thepower and rate, respectively, when the channel state is j and the i^(th)point is selected on the corresponding curve, both weighted by theprobability to be in that channel state. Only one Pareto point can beselected when being in one channel state, resulting in the followingconstraints:

$\begin{matrix}{{{\sum\limits_{i}\; x_{ij}} = 1},{\forall{{j\mspace{31mu} x_{ij}} \in \left\{ {0,1} \right\}}}} & \left( {{equation}\mspace{14mu} 10} \right)\end{matrix}$

For a given rate constraint R, the optimal control policy is thesolution of the following problem:

$\begin{matrix}{{\min{\sum\limits_{i}^{\;}\;{\sum\limits_{j}^{\;}\;{x_{ij}p_{ij}^{\prime}\mspace{14mu}{subject}\mspace{14mu}{to}\mspace{14mu}{\sum\limits_{i}^{\;}\;{\sum\limits_{j}^{\;}\;{x_{ij}r_{ij}^{\prime}}}}}}}} > R} & \left( {{equation}\mspace{14mu} 11} \right)\end{matrix}$

This is the classical multiple choice knapsack problem. One isinterested in the family of control policies corresponding to R rangingfrom 0 to Rmax; Rmax being the maximum average rate achievable on thelink. This family is called the radio link control strategy. Let kjdenote the index of the point selected on the jth Pareto curve.Formally, kj=i

xij=1. A control policy can be represented by the vector k={kj}. Thecontrol strategy is denoted {k(n)} corresponding to the set of point {(R ^((n)), P ^((n)))} in the average rate versus average power plane. Agood approximation of the optimal radio link control strategy (i.e. thatbounds the trade-off between R and P) can be derived iteratively with agreedy heuristic explained in the flowchart shown in Table 4 below.Notice that this derivation is done when the energy-performancetrade-off characteristics per state (the Pareto curves in FIG. 15)change, i.e. if recalibration occurs.

TABLE 4 Let k⁽⁰⁾ be the solution of (11) when R = 0. Obviously itcorresponds to {x_(0j) = 1 ∀j}, i.e. the point of coordinate (0,0) isselected on each Pareto curve. Denote the so-called current policy,k^((n)) = {K_(j) ^((n))}; the next policy k^((n+1)) corresponding to thejust higher rate constraint can be computed as follows: $\begin{matrix}{{{Compute}\mspace{14mu}{the}\mspace{14mu}{slopes}\mspace{20mu} s_{j}^{(n)}} = {\frac{p_{{({k_{j}^{(n)} + 1})}j}^{\prime} - p_{{(k_{j}^{(n)})}j}^{\prime}}{r_{{({k_{j}^{(n)} + 1})}j}^{\prime} - r_{{(k_{j}^{(n)})}j}^{\prime}}{\forall j}}} \\{{{Define}\mspace{14mu}{\hat{j}}^{(n)}} = {\underset{j}{\arg\mspace{14mu}\max}\;\left( s_{j}^{(n)} \right)}}\end{matrix}\quad$ The next policy k^((n+1)) is such ask_(ĵ)^((n + 1)) = k_(ĵ)^((n)) + 1   and  k_(j)^((n + 1)) = k_(j)^((n)), ∀j = ĵ

From the family of policies derived with the greedy heuristic, it ispossible to derive a policy for any average rate constraint (∉{ R^((n))} also). For the given average rate constraint, from the set areselected those policies that lead to a average rate just higher (R_(h))and just lower (R_(l)) than the constraint (R). For a given packettransmission, the configuration knobs are set according to one of thesepolicies. The selection is done randomly with probability P_(h) andP_(l)=1−P_(h), respectively, where P_(h) is given by equation (12)

$\begin{matrix}{P_{h} = \frac{R - R_{l}}{R_{h} - R_{l}}} & \left( {{equation}\mspace{14mu} 12} \right)\end{matrix}$

Doing this, the final average power versus average rate characteristicis the linear interpolation between the trade-off points {( R ^((n)), P^((n)))}.

The performance of the proposed radio link controller is evaluated inthe average rate versus average power plane. Results are depicted inFIG. 16. The trade-off curve achieved by the control strategy derivedwith the greedy heuristic (circles) is compared to the optimal solutionof (eq. 11) for the range of R (plain line). Theoretically, the greedyheuristic is sub-optimal since the Pareto curves in FIG. 15 are notnecessarily convex. Yet, it clearly appears that the optimal solution isclosely approached. This trade-off curve can be used to evaluate thebenefit of the proposed strategy compared with traditional radio linkcontrol aiming at maximizing the data rate under power constraint. Withsuch a strategy, the policy corresponding to the highest feasible ratewould be selected. This corresponds to the point A in FIG. 16. If thelink capacity is not fully used, the link idles and the terminal entersregularly in sleep mode. The average power decreases linearly with theduty cycle. Hence, the corresponding power-rate trade-off is representedby the dashed-dot line in FIG. 16. It can be seen that, in all cases,except the one corresponding to maximal load, the proposed energy awareradio link control strategy saves power when compared with thetraditional strategy. For example at half load, the gain reaches 80%.

In another embodiment the invention relates to wireless communicationsystems that are provided with a sleep mode. Further also the multi-useraspect is introduced in the scheme to manage system-wide powermanagement dimensions at runtime as described below.

In the wireless network as in FIG. 17 multiple nodes are controlledcentrally by an access point (AP). Each node (such as e.g. a handheldvideo camera) wishes to transmit frames at real-time and it is the AP'sresponsibility to assign channel access grants. In an embodiment, theresource allocation scheme within the AP must ensure that the nodes meettheir performance constraints by delivering their data in a timelymanner while consuming minimal energy. More formally the problem can bestated in the following way. The network consists of n flows {F₁, F₂, .. . , F_(n)}. For notational simplicity, a one-to-one mapping of flowsto nodes is assumed, but the design methodology is applicable to one ormore flows per node. Each flow i, 1=i=n, is described by the followingproperties:

(a) Cost Function (C_(i)): This is the optimization objective, e.g. tominimize the total energy consumption of all users in terms ofJoules/job. In for example a video context, a job is the frame size ofcurrent application layer video frame.

(b) QoS Constraint (Q_(i)): The optimization has to be carried outtaking into account a minimum performance or QoS requirement in order tosatisfy the user. As delivery of real-time traffic is of interest (e.g.video streaming), the QoS is described in terms of the job failure rate(JFR) or deadline miss rate. JFR is defined as the ratio of the numberof frames not successfully delivered before their deadline to the totalnumber of frames issued by the application. The QoS constraint isspecified by the user as a target-JFR (JFR*), to be maintained over thelifetime of the flow.

(c) Shared Resource {R₁, R₂, . . . , R_(m)}: Multiple resourcedimensions could be used to schedule flows or tasks in the network, e.g.time, frequency or space. The restricted case is considered here whereaccess to the channel is only divided in time. Therefore, time is thesingle shared resource and is denoted by R. The resource fractionconsumed by the i^(th) node is denoted by R_(i). The maximum timeavailable for any flow is R_(i) ^(max), which is the frame period forperiodic traffic.

(d) Control Dimensions {K₁, K₂, . . . , K_(l)}: For a given wireless LANarchitecture, there are platform independent control knobs or dimensionsas already described before that control the received signal-to-noiseratio related to the resource utilization in terms of the transmissiontime per bit, given the current path loss. The control dimensionsettings are discrete, inter-dependent and together have a non-linearinfluence on the cost function.

(e) System state {S₁, S₂, . . . , S_(s)}: As the environment is verydynamic, the system behavior will vary over time. The environmentalfactors independent of the user or system control are represented by asystem state variable. In a wireless environment with e.g. VBR videotraffic, the system state is determined by the current channel state andthe application data requirements. The scheduling algorithm is executedperiodically based on the channel epoch and the rate at which the datarequirements change. Each flow is associated with a set of possiblestates, which determines the mapping of the control dimensions to thecost and resource.

The following system properties contribute to the structure of themethodology. The key aspects are the mapping of the control dimensionsto cost and resource profiles respectively, and the general propertiesof this mapping. A resource (cost) profile describes a list of potentialresource (cost) allocation schemes needed for each configuration pointK. These profiles are then combined to give a Cost-Resource trade-offfunction, which may be essential for solving the resource allocationproblem.

Cost Profile Properties

-   -   Every flow has a known minimum and maximum cost over all control        dimensions, which is a function of the desired JFR* and the        current system state (e.g. channel state). The cost range        (difference between maximum and minimum) is important as this        determines the impact the control dimensions have on the system        cost.    -   The per-dimension discrete control settings can be ordered        according to their minimal associated Cost.    -   The overall system cost, C, may be defined as the weighted sum        of costs of all flows, where each flow can be assigned a certain        weight depending on its relative importance or to improve        fairness.

$\begin{matrix}{C = {\sum\limits_{i = 1}^{n}\;{w_{i}C_{i}}}} & \left( {{eq}.\mspace{14mu} 13} \right)\end{matrix}$

Resource Profile Properties

-   -   Every flow has a known minimum and maximum resource requirement        across dimensions, which is a function of the desired JFR* and        system state.    -   Depending on the current system constraints and possible        configurations, each flow has a minimum resource requirement        R_(i) ^(min). It is assumed all minimum resource requirements        can be satisfied at each moment in time. Hence, no overload        occurs and all flows can be scheduled. This is a reasonable        assumption under average load and average channel conditions.        However, under worst-case conditions and non-scalable video        applications, a system overload may occur and one or more flows        will need to be dropped. While the policy to drop flows is out        of the scope of the optimization criterion, a practical system        may employ a policing policy that is fair to the users.    -   The per-dimension discrete control settings can be ordered        according to their minimal associated Resource requirement.    -   The overall system resource requirement R is defined as the sum        of the per flow requirements:

$\begin{matrix}{R = {\sum\limits_{i = 1}^{n}\; R_{i}}} & \left( {{eq}.\mspace{20mu} 14} \right)\end{matrix}$

The goal is to assign transmission grants, resulting from an optimalsetting of the control dimensions, to each node such that the per flowQoS constraints for multiple users are met with minimal energyconsumption. For a given set of resources, control dimensions and QoSconstraints, the scheduling objective is formally stated as:

$\begin{matrix}{\min\limits_{C}{\sum\limits_{nodes}^{\;}\;{\omega_{i}{C_{i}\left( S_{i} \right)}}}} & \left( {{eq}.\mspace{14mu} 15} \right)\end{matrix}$

subject to:

$\begin{matrix}{{{JFR}_{i} \leq {JFR}_{i}^{*}},{i = 1},\;\ldots\mspace{11mu},n} & {\left( {{QoS}\mspace{14mu}{Constraints}} \right)\mspace{11mu}} \\{{{\sum\limits_{nodes}R_{i,j}} \leq R_{j}^{\max}},{j = 1},\;\ldots\mspace{11mu},m} & \left( {{Resource}\mspace{14mu}{Constraints}} \right) \\{\left. K_{j}\rightarrow{R_{i}\left( S_{i} \right)} \right.,{i = 1},\;\ldots\mspace{11mu},{n;{j = 1}},\;\ldots\mspace{11mu},l} & \left( {{Resource}\mspace{14mu}{Profiles}} \right) \\{\left. K_{j}\rightarrow{C_{i}\left( S_{i} \right)} \right.,{i = 1},\;\ldots\mspace{11mu},{n;{j = 1}},\;\ldots\mspace{11mu},l} & \left( {{Cost}\mspace{14mu}{Profiles}} \right)\end{matrix}$

The solution of the optimization problem yields a set of feasibleoperating points K, which fulfill the QoS target, respects the sharedresource constraint and minimizes the system cost.

When considering energy-scalable systems, the number of controldimensions is large and leads to a combinatorial explosion of thepossible system configurations as already explained. In addition, theresource and cost profile relations are complex. In order to solve thisproblem efficiently, a pragmatic scheme is needed to select theconfigurations at runtime. This is achieved by first determining theoptimal configurations of all control dimensions at design time. Atruntime, based on the channel condition and application load, the bestoperating point is selected from a significantly reduced set ofpossibilities.

A property of the design-time phase model is that the configurations canbe ordered according to their minimal cost and resource consumption,describing a range of possible costs and resources for the system. Foreach additional unit of resource allocated, one only needs to considerthe configuration that achieves the minimal cost for that unit of theresource. For each possible system state (for different channel andapplication loads), the optimal operating points are determined bypruning the Cost-Resource (C-R) curves to yield only the minimum costconfigurations at each resource allocation point.

A function p_(i): R→C is defined, such thatp _(i)(R _(i)(S _(i)))=min{C _(i)(S _(i))|(K _(i) →R _(i)(S _(i)))

(K _(i) →C _(i)(S _(i)))}which defines a mapping between the Resource and the Cost of a certainconfiguration, k, for a node in a state, S_(i), as shown in FIG. 18.Considering the resulting points in the C-R space, one is onlyinterested in those that represent the optimal trade-off between theenergy and resource needs for the system. Indeed, the trade-off betweentransmission time and transmission energy is a fundamental property forwireless communication, bounded by Shannon. Although the discretesettings and non-linear interactions in real systems lead to a deviationfrom this optimal trade-off, it can be well approximated as follows.

The convex minorant of these pruned curves is calculated along both forthe Cost and Resource dimensions, and the intersection of the result isconsidered. As a result, the number of operating points is reducedsignificantly thus rendering it very useful for the runtime optimization(FIG. 18).

Several trade-offs are present in the system: increasing the modulationconstellation size decreases the transmission time but results in ahigher packet error rate (PER) for the same channel conditions and PAsettings. In an embodiment, the energy savings due to decreasedtransmission time must offset the increased expected cost ofre-transmissions. Also, increasing the transmit power increases thesignal distortion due to the PA. On the other hand, decreasing thetransmission power also decreases the efficiency of the PA. Similarly,it is not straightforward when using a higher coding gain, if thedecreased SNR requirement or increased transmission time dominates theenergy consumption. Finally, considering the trade-off between sleepingand scaling: a longer transmission at a lower and more robust modulationrate needs to compensate for the opportunity cost of not sleepingearlier.

A greedy algorithm is employed to determine the per-flow resource usageR_(i) for each application to minimize the total system cost C. Thealgorithm traverses all flows' Cost-Resource curves and at every stepconsumes resources corresponding to the maximum negative slope acrossall flows. This ensures that for every additional unit of resourcesconsumed, the additional cost saving is maximum across all flows. Thecurrent channel state and application demand are assumed to be known foreach node. This information is obtained by coupling the MAC protocolwith the resource manager and is explained in the next section. By theassumption that sufficient resources are available for all flows, theoptimal additional allocation to each flow, R_(i)>0, 1≦i≦n, subject to

${\sum\limits_{i = 1}^{n}\; R_{i}} \leq R$is determined.

The following greedy algorithm is used:

-   -   a. Allocate to each flow the smallest resource possible for the        given state, R_(min). All flows are schedulable under worst-case        conditions, hence

${\sum\limits_{i = 1}^{n}\; R_{\min}} \leq {R.}$

-   -   b. Let the current normalized allocation of the resource to        flow, F_(i), be R_(i), 1=i=n. Let the unallocated quantity of        the available resource be R₁.    -   c. Identify the flow with the maximum negative slope,        |C_(i)′(R_(i|))|—representing the maximum decrease in cost per        resource unit. If there is more than one, pick one randomly. If        the value of the minimum slope is 0, then stop. No further        allocation will decrease the system cost further.    -   d. Increase R_(i) by the amount till the slope changes for the        i^(th) flow. Decrement R_(l) by the additional allocated        resource and increment the cost C by the consequent additional        cost. Return to step b until all resources have been optimally        allocated or when R_(l) is 0.

In this implementation, the configuration points at design-time aresorted in the decreasing order of the negative slope between twoadjacent points. The complexity of the runtime algorithm is O(n. log L)for n nodes and L configuration points per curve. For a given channeland frame size, the number of configuration points to be considered atruntime is relatively small.

Taking into account that the relation C_(i)(R_(i)) derived at designtime is a convex trade-off curve, it can be shown easily that the greedyalgorithm leads to the optimal solution for continuous resourceallocation. The proof can be extended for real systems with discreteworking points to show that the solution is within bounded deviationfrom the optimal. For a real system, however, the settings for differentcontrol dimensions such as modulation or transmit power are in discreteunits. This results in a deviation from the optimal resource assignment.The worst-case deviation from the optimal strategy is bounded and small.

Again the practical example of an OFDM-based wireless LAN system is nowconsidered. As previously discussed, several control dimensions can beidentified that enable to trade-off performance for energy savings andvice versa. As above the power amplifier back-off (P_(back-off)), thepower amplifier transmit power (P_(TX)), the modulation (N_(Mod)) andthe code rate (B_(c)) are considered.

To determine the Job Failure Rate and total expected energy consumption,it may be essential to consider the system dynamics, which include thecurrent channel condition and the application demand. The current jobsize (in number of fragments) varies significantly for video traffic. Inaddition, just considering the average received SINAD is not sufficientto characterize the channel for wireless OFDM systems wherefrequency-selective fading is important. A time-varying channel model isconsidered and expressions are derived relating the average packet errorrate (PER), the JFR and expected energy consumption.

1) Traffic Model

As the goal is to provide timeliness (QoS) guarantees while minimizingenergy consumption for a target performance, periodic delay-sensitivetraffic is considered. Both constant bit rate (CBR) and variable bitrate (VBR) traffic is studied, in order to show the impact of thedynamics. VBR traffic consists of MPEG-4 flows. A Transform ExpandSample (TES) based MPEG-4 traffic generator that generates traffic withthe same first and second order statistics as an original MPEG-4 traceis used. All fragmentation is done at the link layer and if a frame isnot completely delivered to the receiver by its deadline, it is dropped.All applications employ UDP over IP.

Each frame size in fact maps to a different system state. A frame sizeis determined in a number of MAC layer fragments, which is assumed to be1024 bytes long for this experiment. From the results, it is observedthat for a given frame size, extrapolating the results for a curvewithin five fragments results in a very low approximation error. As themaximum frame size is assumed to be 50 fragments long in the testsconsidered, Cost-Resource curves are only constructed for 1, 2, 3, 4, 5,10, 20, 30, 40, 50 fragments per frame.

2) Channel Model

A frequency selective and time varying channel model is now used tocompute the PER for all transceiver knob settings. An indoor channelmodel based on HIPERLAN/2 was used for a terminal moving uniformly atspeeds between 0 to 5.2 km/h (walking speed). This correspondstheoretically to a coherence time of approximately 28 ms. A set of 1000time-varying frequency channel response realizations (sampled every 2 msover one minute) were generated and normalized in power. Data wasencoded using a turbo coder model and the bit stream was modulated using802.11a OFDM specifications. For a given back-off and transmit power,the SINAD at the receiver antenna was computed as before. A path-loss of80 dB at a distance of 10 m is assumed.

The signal was then equalized (zero-forcing scheme), demodulated anddecoded. From the channel realization database, a one-to-one mapping ofSINAD to receive block error rate was determined for each modulation andcode rate. The channel was then classified into 5 classes (FIG. 19A). Inorder to derive a time-varying link-layer error model, we associate eachchannel class to a Markov state, each with a probability of occurrencebased on the channel realizations database (FIG. 19B). Given thisfive-state error model, the PER can be efficiently modelled fordifferent configurations at runtime. The Packet Error Rate (PER) isobtained by assuming the block errors follow a binomial process for apacket size of L_(frag) bits and a block size of 288 bits (see alsoTable 2):PER=[1−(1−BlER)^(L) ^(frag) ^(/288)]  (eq. 16)

Now the exact mapping of the control dimensions K to the cost andresource dimensions is derived, based on these expressions and thesystem state. When delivering a frame, different transmission andretransmission strategies can be used, each resulting in a differenttotal expected energy and time to send the frame, and each resulting inanother expected failure rate for the frame. To simplify, the policy isadopted that each fragment of a frame should be transmitted orretransmitted using the same configuration K. This is a goodapproximation for the real optimal transmission strategy, which includesadapting the strategy depending on the outcome of a fragmenttransmission (conditional recursion which is complex to solve). For theapproximation, a recursive formulation can be derived to compute theexpected energy E_(K), the timeslot needed TXOP_(K), and the expectedfailure rate JFR_(K), for each system state. The MAC protocol overheadis taken into account in this mapping, and the parameters, which arebased on 802.11 e, are listed in Table 2. A Contention Free accessscheme is considered for the transmissions.

Consider a frame, which consists of m fragments or packets and has to bedelivered during a known channel state C_(S). The tuple (C_(S),m) isdefined as the system state. All following expressions for cost andresource consumption, hence, depend not only on the configuration K, butalso on the system state. For notational simplicity, the state index isomitted. Each packet is transmitted with configuration K, for which thePER_(K) can be determined, based on the models derived above. Theprobability that the frame is delivered successfully with exactly (m+n)transmissions (hence n retransmissions), is given by the recursion:

$\begin{matrix}{{S_{n}^{m}(K)} = {\sum\limits_{i = 1}^{\min{({m,n})}}\;{C_{i}^{m} \times \left( {PER}_{K} \right)^{i} \times \left( {1 - {PER}_{K}} \right)^{m - i} \times {S_{n - i}^{i}(K)}}}} & \left( {{eq}.\mspace{14mu} 17} \right) \\{{S_{0}^{m}(K)} = \left( {1 - {PER}_{K}} \right)^{m}} & \left( {{eq}.\mspace{14mu} 18} \right)\end{matrix}$in which C_(i) ^(m) denotes the number of possibilities to select iobjects out of m. Hence, the probability to deliver the frame consistingof m fragments correctly with maximum n re-transmissions is

$\begin{matrix}{{1 - {{JFR}_{n}^{m}(K)}} = {\sum\limits_{j = 0}^{n}\;{S_{j}^{m}(K)}}} & \left( {{eq}.\mspace{14mu} 19} \right)\end{matrix}$Here only data losses are assumed to result in job failures. As controlframes are much shorter and less susceptible to errors, it is assumedthey do not suffer from packet errors.

In order to determine the expected energy and time needed to deliver aframe with m fragments and n retransmissions, one needs to know theoverhead due to the MAC protocol for a successful and a failedtransmission, i.e. E_(good), E_(bad), T_(good) and T_(bad). As a 802.11eHCF-type MAC is assumed, a successful and failed data transmissionfollow FIG. 20. Let E_(ACK) be the energy needed to receive an ACKpacket, and T_(ACK) the time needed for the ACK, E_(Header) andT_(Header) are the overheads for the MAC and PHY headers.E _(good)(K)=E _(K) +E _(Header)+(2×T _(sifs) ×P _(Idle))+E_(ACK)  (eq.20)E _(bad)(K)=E _(K) +E _(Header)+((2×T _(sifs) +T _(ACK))×P_(Idle))  (eq.21)T _(good)(K)=T _(K) +T _(Header)+(2×T _(sifs))+T _(ACK)  (eq.22)T _(bad)(K)=T _(good)(K)  (eq.23)The time needed to send m fragments with max n retransmissions, forconfiguration K, is then:TXOP _(n) ^(m)(K)=[m×T _(good)(K)]+[n×T _(bad)(K)]  (eq.24)

The average energy needed to transmit m fragments, with maximum nretransmissions, and configuration K is a bit more complex. The reasonis that one is interested in the expected energy, taking into accountthe chance that a retransmission should happen or not:

$\begin{matrix}{{E_{n}^{m}(K)} = {\sum\limits_{j = 0}^{n}\;{{S_{n}^{m}(K)} \times \left( {\left( {m \times {E_{good}(K)}} \right) + \left( {j \times {E_{bad}(K)}} \right)} \right)}}} & \left( {{eq}.\mspace{14mu} 25} \right)\end{matrix}$

It is the sum of the probability that the transmission will succeedafter m good and j bad transmissions, times the energy needed for thesegood and bad transmissions. In order to have the correct expected energyconsumption, a second term should be added to denote the energyconsumption for a failed job, hence when there are less than m goodtransmissions, and (j+1) bad ones:

$\begin{matrix}{{E_{n}^{m}(K)} = {{E_{n}^{m}(K)} + {{{JFR}_{n}^{m}(K)} \times \left\lbrack {{E_{bad}(K)} + {\sum\limits_{j = 1}^{m}\;{{S_{n}^{j}(K)} \times}}} \right.}}} & \left( {{eq}.\mspace{14mu} 26} \right) \\\left. \mspace{101mu}\left( {\left( {j \times {E_{good}(K)}} \right) + \left( {n \times {E_{bad}(K)}} \right)} \right) \right\rbrack & \;\end{matrix}$

As a result, the E, TXOP, and JFR can be determined as a function offrame size, channel state and number of retransmissions for eachconfiguration K. This determines the full cost and resource profile forthe system. In FIGS. 21A and 21B, the impact of the PA control knobs (PAback-off and PA transmit power) on the resource (TXOP) and cost (energy)is illustrated. Only the mapping that corresponds to the smallest TXOPand energy consumption is plotted (hence the mapping with the smallestnumber of retransmissions that achieves the required JFR* of 10e-3).FIGS. 22A and 22B shows the merged and pruned energy-TXOP curves fordifferent channel states and different frame sizes, respectively. Onecan see that the total range in energy consumption is large, both withinand across system states. The large trade-off illustrates the fact thattraditional systems, which are designed for a fixed and worst castscenario, result in a significant energy waste.

Determining a schedule that achieves the required performance withminimal energy consumption is challenging because the instantaneous loadand channel state vary independently for each node. Previously theEnergy and TXOP were determined needed to deliver a job depending on thecurrent state, which is the frame size and the channel state. Based onthese curves for each node, the scheduler can derive a near-optimalallocation at run-time. There needs to be a feedback loop between thenodes and the scheduler in the AP. The current state information needsto be collected by the scheduler and the decisions about the channelaccess grants should be communicated back to the nodes with minimaloverhead. It is now shown how a sleep-aware Medium Access Controller cantake care of this.

The MAC is responsible for resource allocation of the shared channelamong different users. The packet-scheduling algorithm in the AP decideswhich node is to transmit, when, and for how long. The focus of framescheduling is on the particular cases of frames sent from a node to itsassociated AP (uplink) and also from one node directly to another andnot via the commonly associated AP (peer-to-peer), as in FIG. 17. As allnodes are assumed to be within the transmission range of the AP, the APcan only schedule one peer-to-peer communication simultaneously. Allnodes communicate only when they have a valid channel access grant ortransmit opportunity (TXOP) or timeslot from the AP.

In order to instruct a node to sleep for a particular duration, the APneeds to know when the next packet must be scheduled. Waking a nodeearlier than the schedule instance will cause it to waste energy in theidle state. Waking the node later than the schedule instance, will causeit to miss the packet's deadline or waste system resources. Thesleep-aware MAC protocol therefore employs two techniques to eliminatedata dependency due to the application and channel. By buffering oneframe, the AP can instruct the node to sleep from the time it wasinformed that the frame arrived to its scheduling instance. The AP stillneeds to poll the node upon frame arrival and therefore this onlypermits the node to sleep between the packet arrival instance and thepacket schedule instance.

Buffering just two frames informs the AP of the current traffic demandbut also the demand in the next scheduling instance. As shown in FIG.23, the AP now only needs to communicate with the node at schedulinginstances. As the real-time stream's packets are periodic, all idle timebetween transmission instances is eliminated for the ideal scheme.

In order to inform the AP of the instantaneous channel and the requiredapplication load for the current and next scheduling instances, oneneeds to employ a link-layer feedback mechanism. This is accomplished byadding just three bytes in the MAC header for the current channel stateand the two buffered frame sizes. Protocols such as 802.11e providesupport for channel information and queue sizes therefore require onlyminor modifications. In every transmission to the AP, the nodecommunicates its channel state and packet sizes of the two heads of theline packets. In the acknowledgement, the AP instructs the node to sleepuntil the time of the next scheduling instance and also assigns it theduration of its next TXOP or resource slot. The scheduling decision is,thus, made every frame period (e.g. 30 ms for high-quality video) of theflow in the system with the highest frame rate.

The energy savings are now verified over a range of practical scenarios.For all results presented here, the target JFR* is set to 10⁻³ which isa reasonable value for wireless links. First the expected energy savingsare analyzed across all channel states and the entire range of systemloads. While this is profiled at design-time, it provides insight as to(a) the range and variation of energy savings with the system dynamicsand (b) the contributions of energy savings from sleeping and scalingunder different conditions. Following this, the energy savings atruntime are studied for both constant bit rate (CBR) and MPEG-4 videoflows under different channel conditions and different system loads. Inorder to evaluate the relative performance of MEERA, the followingcomparative transmission strategies are considered:

-   1. MEERA: This is the optimal operating scheme considering the    energy trade-off between sleep and scaling. The operating point is    determined from the C-R curves and the runtime algorithm described    previously.-   2. MEERA-no sleep: This scheme uses the C-R curves to determine the    optimal TXOP when no sleeping is possible. The same runtime    algorithm is used and the nodes remain in the idle state after    completion. The purpose of this case is to show the contribution of    sleeping under different loads.-   3. Fixed: In this scheme the back-off and output power are fixed to    the highest setting and use take the highest feasible modulation and    code rate that will successfully deliver the packets. After    successful transmission, one goes to sleep. This case is the    approach where no scaling is employed and the sleep duration is    maximized.-   4. Fixed—no sleep: This scheme is similar to Fixed, but the    transceiver remains in the idle mode after successful transmission.    This is the base operating scheme of current wireless LAN    transceivers with no power save features enabled.

Consider the C-R curves in FIG. 22, for transmitting a one-fragmentsized frame over different channel states. If the TXOP assignment forthe user is distributed uniformly over time, the expected energyconsumption is proportional to the area under the C_(i)(R_(i)) curve foreach channel state shown in FIG. 22( a). Similarly, the C-R curves arederived for the three comparative transmission strategies discussedabove. The C_(i)(R_(i)) curve, for the fixed case, for example, is infact a fixed point (at small TXOP), and the expected energy consumptionproportional to the area under a horizontal line through this point. InFIG. 24A, the relative energy consumption (normalized by the maximumenergy consumed by Fixed over all cases), is plotted for the fourschemes over different channel states. As expected, MEERA outperformsthe other techniques since it takes advantage of the energy that can besaved by both sleeping and TXOP scaling. The energy needed to transmit aunit of data increases from best to worst channel state due to acombination of (a) the lower modulation rate necessary to meet thehigher SINAD requirement (hence shorter sleep duration), (b) a higherrequired output power to account for the worse channel and (c) theincreased cost of retransmissions. It can be observed, for example, forthe best channel state, the energy consumption is low for both the Fixedand MEERA approaches. The energy gains for this channel state primarilyresult from sleeping. On the other hand, for the worst channel state,the transmission energy becomes more dominant, the energy gains due toTXOP scaling are more significant. When looking at the energy gainscontributed by sleeping and scaling over a range of link utilizations,the energy gains due to TXOP scaling can be expected to dominate whenthe transmission duration is large when compared to the sleep interval.Hence, for larger frame sizes or at higher per-flow link utilization,the relative energy saving due to scaling has a grater influence. Thisobservation is illustrated in FIG. 24B, where the relative gain for thedifferent techniques—compared to the Fixed-no sleep case—are plottedover a series of frame sizes (in terms of number of 1024 bytefragments), for channel state 1. Indeed, for a 5-fragment frame TXOP,scaling and sleeping (represented by MEERA-No sleep and Fixed-sleepingrespectively) have a comparable performance. For larger frame sizes, theTXOP scaling in MEERA-No sleep contributes significantly to the energysavings.

Now a multiple-user scenario is considered where the TXOP assignmentsare not uniformly distributed but based on the user's application datarate requirement and the constraints enforced by other users sharing thesame link. The influence is now discussed of the aggregate linkutilization on the per-flow energy consumption for CBR flows over astatic channel. The effective throughput of 802.11e, after consideringprotocol overheads for the first channel state, is approximately 30 Mbpswhen the highest modulation constellation is used. In the experimentillustrated by FIG. 25A, the link utilization is increased in steps of 2Mbps CBR flows up to the maximum link capacity. The per-flow energyconsumption of MEERA increases as the aggregate system load increases.At higher loads due to a large number of flows, a smaller TXOP from theC-R curve is assigned to each flow resulting in higher per flow energyconsumption. The difference with the case where only TXOP scaling isused (MEERA-no sleep) is most noticeable since the possibility to scaleis reduced with increasing system load. In a multi-user scenario it isalways beneficial to include sleeping as it is influenced to a lesserextent by aggregate load increases due to other flows. On the otherhand, if the load is increased by increasing the per-flow throughputrequirement for a fixed number of flows, the gain of scaling dominatesover sleeping. In FIG. 25B, the average per-flow data rate requirementis increased for an MPEG-4 flow (in a network with five such flows). Inthis case, the reduction of sleep duration increases the energyconsumption only slightly as the energy saving from scaling startcontributing more. This is evident when comparing MEERA with the energyconsumption of Fixed, which increases almost linearly with the datarate. It is important to note that as the system is not forced intooverload, only moderate link utilization is considered (<70% averagelink utilization using the highest transmission rate) for MPEG-4 flows.

Now the energy consumption trends are considered for a time-varyingchannel. A 5-user scenario is used to understand the impact of dynamicchannel variations on energy consumption. The channel variesindependently over all the users on a frame-by-frame basis. In FIG. 26A,the total system load is increased from 2.5 Mbps to 10 Mbps for five CBRflows. When compared to the same system load where the channel is in thebest (fixed) state, an increase in energy consumption is observed. Thisis because during every scheduling cycle, the flows experiencing worsechannel states require more transmission time (due to lowerconstellation) and therefore consume more energy. In addition, theyforce the other flows to transmit in a smaller TXOP and increase theirenergy consumption too. Further, in FIG. 26B the energy savings achievedwhen compared to Fixed-no sleep over different system load are shown.Sleeping results in an energy saving for all loads, which is obvious ina multi-user scenario. However, comparing the gain MEERA achieves to theFixed approach (with sleeping), MEERA results in a significantadditional gain of a factor 2 to 5 depending on the system load. Thecombination of sleep and scaling in MEERA yields an overall system gainfactor from 2 to 9.

FIG. 27 is a flowchart of a process for managing the operation of atelecom system in accordance with an embodiment of the invention.According to the embodiment, the process comprises determining a rateconstraint and a telecom environment condition (S2701) and providingpredetermined working points per discrete set of telecom environmentcondition (S2703). The process may further include determining thepredetermined working points. Then, a working point is selected from thepredetermined working points (S2705), and the telecom system is operatedat the selected working point (S2707). Prior to the selection of theworking point, the process may further include loading and adapting thepredetermined working points.

1. A method of managing the operation of a telecom system according totelecom environment conditions at run-time, the telecom systemcommunicating across one or more particular channels and comprising atleast two of an application layer, a middleware layer, a transportlayer, a network layer, a data link layer, and a physical layer, themethod comprising: accessing a plurality of configuration points thathave been determined with a simulation framework using an optimizationmethod applied across at least two layers of the telecom system forsimulated telecom environments, wherein the simulation framework isseparate from said telecom system; determining a rate constraint atrun-time; determining a set of selectable configuration points from theplurality of configuration points based on the rate constraint;determining one or more telecom environment conditions of thepropagation channel being used in the telecom environment at run-timebased on channel state information of the particular channels; selectinga configuration point from the set of selectable configuration points,wherein the selected configuration point is selected based on saidtelecom environment conditions, wherein said plurality of predeterminedconfiguration points are given for a discrete set of telecom environmentconditions; and operating said telecom system at said selectedconfiguration point by setting control parameters corresponding to saidselected configuration point, wherein the telecommunication subsystemcontinues to communicate across the particular channels.
 2. The methodof claim 1, wherein said telecom system is a single telecom device. 3.The method of claim 1, wherein said telecom system is a plurality oftelecom devices.
 4. The method of claim 3, wherein operating saidtelecom system comprises setting control parameters corresponding tosaid selected configuration point for each telecom device.
 5. The methodof claim 3, wherein said telecom environment conditions comprise atleast one of path loss and channel frequency selectivity.
 6. The methodof claim 3, wherein said selecting of a configuration point comprisessolving a scheduling problem.
 7. The method of claim 6, wherein saidscheduling involves scheduling transmission of packets between two ormore of said telecom devices, said scheduling taking into accountdependencies between packets.
 8. The method of claim 1, wherein saidplurality of predetermined configuration points define a monotonic,non-convex curve.
 9. The method of claim 1, wherein said selecting of aconfiguration point further comprises selecting the plurality ofpredetermined configuration points corresponding to the determinedtelecom environment conditions.
 10. The method of claim 1, whereinbefore said selecting of a configuration point, the method furthercomprises determining said plurality of predetermined configurationpoints.
 11. The method of claim 1, wherein the telecom environmentconditions comprise path loss, channel frequency selectivity, or currenttraffic requirements.
 12. The method of claim 1, wherein after loading,the method further comprises adapting said plurality of configurationpoints.
 13. The method of claim 1, wherein said predetermined set ofconfiguration points comprises a sleep mode and a working mode of saidtelecom device.
 14. The method of claim 1, wherein said rate constraintis a constraint on an average rate.
 15. The method of claim 1, whereinsaid telecom system is a wireless telecom system.
 16. The method ofclaim 1, wherein the rate constraint is passed from one layer toanother.
 17. The method of claim 1, wherein said control parameterscomprise one or more parameters selected from the group consisting ofcontrolling modulation order, code rate, transmit power and packet size.18. The method of claim 1, wherein said telecom environment conditionscomprise a channel state.
 19. The method of claim 18, wherein one ormore performance-energy trade-off curves are derived for each of saidchannel states.
 20. The method of claim 19, wherein the energy-per-bitis used as an energy metric in the performance-energy trade-off curves.21. The method of claim 19, wherein net throughput is used as aperformance metric in the performance-energy trade-off curves.
 22. Themethod of claim 18, wherein a sum of energy consumption of said telecomdevices is used as an energy metric in the performance-energy trade-offcurves.
 23. The method of claim 19, wherein said performance-energytrade-off curves are Pareto-optimal curves.
 24. The method of claim 1,wherein said telecom environment conditions comprise current trafficrequirements, and wherein said current traffic requirements is takeninto account in determining said rate constraint.
 25. The method ofclaim 1, wherein said rate constraint is a varying rate constraint. 26.The method of claim 1, wherein said selecting is performed by solving anoptimization problem.
 27. The method of claim 1, wherein said discreteset of telecom environment conditions is organized per channel state.28. A telecommunication device, configured to perform the method ofclaim 1, wherein said selecting comprises solving an optimizationproblem.
 29. The device of claim 28, further comprising a processingmechanism configured to solve said optimization problem, or determine oradapt said predetermined configuration points.
 30. The method of claim1, wherein the simulated telecom environments include simulated channelconditions.
 31. A telecommunication device, comprising a memory forstoring said performance/energy trade-off curves, the device configuredto perform the method of claim
 1. 32. A method of managing the operationof a telecom system according to telecom environment conditions atrun-time, the telecom system communicating across a propagation channeland comprising a queuing mechanism introducing a queuing delay, thetelecom system further comprising at least two of an application layer,a middleware layer, a transport layer, a network layer, a data linklayer, and a physical layer said method comprising: accessing aplurality of control parameters that have been determined with asimulation framework using an optimization method applied across atleast two layers of the telecom system for simulated telecomenvironments, wherein the simulation framework is separate from saidtelecom system; determining one or more conditions of the propagationchannel being used in the telecom environment at run-time based onchannel state information of the propagation channel, the one or moreconditions comprising current traffic requirements; determining anaverage rate constraint to meet said current traffic requirements bysolving an optimization problem and selecting at least one controlparameter, said control parameter taking into account the instantaneousqueuing delay, wherein the optimization problem is bounded by theaverage rate constraint, and wherein the at least one control parameteris selected from the plurality of control parameters; and operating saidtelecom system by setting said control parameter taking into account theinstantaneous queuing delay, wherein the telecommunication subsystemcontinues to communicate across the propagation channel.
 33. The methodof claim 32, wherein determining said average rate constraint is basedon a look-ahead with a variable window size of link utilization, saidwindow size being determined by said selected control parameter.
 34. Atelecommunication device, comprising storage means for storing saidperformance/energy trade-off curves, the device configured to performthe method of claim
 32. 35. The method of claim 32, wherein said telecomdevice is further provided with a packet retransmission mechanism andwhereby said optimization problem optimizes the end-to-end throughput.36. The method of claim 32, wherein said average rate constraint is setas a parameterizable function of the number of bits in the queue of saidqueuing mechanism.
 37. The method of claim 36, wherein theparameterizable function comprises the following equation:R _(i) =K×Δ _(i) wherein R _(i) denotes an average rate constraint;wherein Δ_(i) denotes an average delay; wherein i denotes a time index;and wherein K is a proportionality factor.
 38. The method of claim 32,wherein the average rate constraint is passed from one layer to another.39. The method of claim 32, wherein the control parameters comprise oneof controlling modulation order, code rate, transmit power, or packetsize.
 40. The method of claim 32, wherein the simulated telecomenvironments include simulated channel conditions.
 41. A non-transitorycomputer readable medium storing a program configured to execute amethod of managing the operation of a telecom communicating across apropagation channel system according to telecom environment conditionsat run-time, the telecom system comprising a queuing mechanismintroducing a queuing delay, the telecom system further comprising atleast two of an application layer, a middleware layer, a transportlayer, a network layer, a data link layer, and a physical layer, saidmethod comprising: accessing a plurality of control parameters that havebeen determined with a simulation framework using an optimization methodapplied across at least two layers of the telecom system for simulatedtelecom environments, wherein the simulation framework is separate fromsaid telecom system; determining one or more conditions of thepropagation channel being used in the telecom environment at run-timebased on channel state information of the propagation channel, the oneor more conditions comprising current traffic requirements; determiningan average rate constraint to meet said current traffic requirements bysolving an optimization problem and selecting at least one controlparameter, said control parameter taking into account the instantaneousqueuing delay, wherein the optimization problem is bounded by theaverage rate constraint, and wherein the at least one control parameteris selected from the plurality of control parameters; and operating saidtelecom system by setting said control parameter taking into account theinstantaneous queuing delay, wherein the telecommunication subsystemcontinues to communicate across the particular channels.
 42. Thecomputer readable medium of claim 38, wherein the method furthercomprises passing the average rate constraint from one layer to another.43. The computer readable medium of claim 41, wherein the controlparameters comprise one of controlling modulation order, code rate,transmit power, or packet size.
 44. The computer readable medium ofclaim 41, wherein the conditions comprise path loss, channel frequencyselectivity, or current traffic requirements.
 45. The computer readablemedium of claim 41, wherein the simulated telecom environments includesimulated channel conditions.
 46. A non-transitory computer readablemedium storing a program configured to execute a method of operating atelecom system according to telecom environment conditions at run-time,the telecom system communicating across a propagation channel andcomprising at least two of an application layer, a middleware layer, atransport layer, a network layer, a data link layer, and a physicallayer, the method comprising: accessing a plurality of configurationpoints that have been determined with a simulation framework using anoptimization method applied across at least two layers of the telecomsystem for simulated telecom environments, wherein the simulationframework is separate from said telecom system; determining a rateconstraint at run-time; determining a set of selectable configurationpoints from the plurality of configuration points based on the rateconstraint; determining one or more conditions of the propagationchannel being used in the telecom environment at run-time based onchannel state information of the propagation channel; selecting aconfiguration point from the set of selectable configuration points,wherein the selected configuration point is selected based on saidtelecom environment conditions; and operating said telecom system atsaid selected configuration point by setting control parameterscorresponding to said selected configuration point, wherein thetelecommunication subsystem continues to communicate across thepropagation channel.
 47. The computer readable medium of claim 46,wherein the conditions comprise path loss, channel frequencyselectivity, or current traffic requirements.
 48. The computer readablemedium of claim 46, wherein the method further comprises passing therate constraint from one layer to another.
 49. The computer readablemedium of claim 46, wherein each configuration point is related to a setof control parameters of the telecom system.
 50. The computer readablemedium of claim 49, wherein each of the control parameters comprise oneof controlling modulation order, code rate, transmit power, or packetsize.
 51. The computer readable medium of claim 46, wherein thesimulated telecom environments include simulated channel conditions.